Our experiments have demonstrated that data-driven summary features of time series learnt for SBI can match the performance of sufficient statistics in the Gaussian linear case, while being significantly better on a non-linear stochastic model. The YuleNet architecture that we present, which is simply a well calibrated one-dimensional CNN, learns adequate summary features for inference while using less parameters and requiring less computational power (measured via MACs) to train than another proposal from the literature. Using the SNPE-C procedure to minimize (REF ) and a normalizing flow for \(q_{\phi }\)  has given satisfactory results, but other options are possible too, such as using MDNs [1]} to approximate probability distributions and employing other LFI strategies as in [2]} and [3]}.
